Extra CanalsNCT07689708
AI Detection Model of Extra Root Canals in Mandibular Premolars Using CBCT Scans
A study testing AI Model to detect any extra canals in mandibular premolars in people with Extra Canals, run by Cairo University.
Trial summary
Successful endodontic treatment depends on the complete identification and management of the entire root canal system. Missed root canals are a major cause of endodontic failure, particularly in mandibular premolars, which exhibit considerable anatomical variability and may contain additional root canals that are… Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Extra Canals
- What is being tested?
- AI Model to detect any extra canals in mandibular premolars
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 272 participants
- Where
- No sites listed yet
- Study dates
- 15 Jul 2026to 10 Jul 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- CBCT scans of mandibular molars of Egyptian patients aging from 18 to 65 years old
- Small Field of view (FOV) including maximum a quadrant
- Voxel size not larger than 2mm
- Mandibular premolars showing complete root formation
- Carious or non-carious teeth
- Absence of artifacts.
Who cannot join
- Mandibular first and second premolars with developmental anomalies, external or internal root resorption, root canal calcification, previous root canal treatment, post restorations, and/or root caries
- CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Diagnostic Accuracy of a Deep Learning Model (Artificial Intelligence) for Detecting Extra Root Canals in Mandibular Premolars on CBCT Images: Diagnostic Accuracy Study.
Successful endodontic treatment depends on the complete identification and management of the entire root canal system. Missed root canals are a major cause of endodontic failure, particularly in mandibular premolars, which exhibit considerable anatomical variability and may contain additional root canals that are difficult to detect using conventional diagnostic methods.
Cone Beam Computed Tomography (CBCT) provides three-dimensional visualization of root canal anatomy and has significantly improved the detection of anatomical variations. However, interpretation of CBCT images remains dependent on the experience and expertise of the clinician, leading to potential observer variability and missed diagnoses.
Read more
Recent advances in artificial intelligence (AI), particularly deep learning models based on convolutional neural networks, have shown promising results in dental image analysis and diagnostic support. AI-assisted diagnostic systems may improve the accuracy, consistency, and efficiency of CBCT interpretation by automatically identifying complex anatomical structures.
The aim of this retrospective diagnostic accuracy study is to evaluate the performance of a newly developed deep learning model for the detection of extra root canals in mandibular premolars using CBCT images. The diagnostic accuracy of the AI model will be assessed by comparing its findings with the assessments of experienced oral and maxillofacial radiologists, which will serve as the reference standard.
A total of 272 CBCT scans of mandibular premolars from Egyptian patients will be included according to predefined eligibility criteria. Diagnostic performance will be evaluated using measures including sensitivity, specificity, positive predictive value, and negative predictive value.
The findings of this study may provide evidence regarding the clinical applicability of AI-assisted diagnostic tools in endodontics and contribute to improved detection of complex root canal anatomy, reduced incidence of missed canals, and enhanced treatment outcomes.
Read the detailed description
The goal of this observational study is to evaluate whether a deep learning artificial intelligence (AI) model can accurately detect extra root canals in mandibular premolars using Cone Beam Computed Tomography (CBCT) images in Egyptian patients. The main questions it aims to answer are:
- Can the AI model accurately detect extra root canals in mandibular premolars on CBCT scans?
- Is the diagnostic accuracy of the AI model comparable to that of experienced oral and maxillofacial radiologists? Researchers will compare the results generated by the AI model with the assessments of experienced radiologists, which will serve as the reference standard.
Participants will:
- Provide previously acquired CBCT scans that meet the study eligibility criteria.
- Have their CBCT images analyzed by the AI model.
- Have their CBCT images independently evaluated by experienced radiologists for comparison with the AI findings.
The study findings may help determine the potential role of AI-assisted diagnostic tools in improving the detection of complex root canal anatomy and supporting endodontic diagnosis
- Conditions
- Extra Canals
- Being tested
- AI Model to detect any extra canals in mandibular premolars
- Keywords
- AI, Mandibular premolars, extra canal
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI Model to detect any extra canals in mandibular premolarsDiagnostic test
It is a study to detect the diagnostic accuracy of AI model to detect extra canals in mandibular premolars
| Study group | Role | What they receive |
|---|---|---|
| Mandibular premolars with single canals | Experimental | Not described Receives: Diagnostic Test: AI Model to detect any extra canals in mandibular premolars |
| Mandibular premolars with more than one canal | Experimental | Not described Receives: Diagnostic Test: AI Model to detect any extra canals in mandibular premolars |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of the Deep Learning Model for Detection of Extra Root Canals in Mandibular Premolars
Diagnostic accuracy of the AI model will be determined by comparison with expert radiologist assessment.
Measured: During the procedure
Secondary outcomes (1)
Sensitivity of the AI Model Specificity of the AI Model Positive Predictive Value (PPV) Negative Predictive Value (NPV)
Measured: During the procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jul 2026
- Study start15 Jul 2026 (estimated)
- Primary completion15 Aug 2026 (estimated)
- Study completion10 Jul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Extra CanalsStudies of the same condition, open and under way.
- More trials run by Cairo UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- It's an AI model to detect extra canals in mandibular premolars
- Masking
- None (open label)
- Enrolment
- 272 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ayah Tarek El Sayed Abudlaah, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07689708
- Sponsor's protocol ID
- 7.1.1
- Status last verified
- Jul 2026
- First submitted
- 25 Jun 2026
- Last updated
- 8 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Mansour S, Anter E, Mohamed AK, Dahaba MM, Mousa A. Two step approach for detecting and segmenting the second mesiobuccal canal of maxillary first molars on cone beam computed tomography (CBCT) images via artificial intelligence. BMC Oral Health. 2025 Sep 8;25(1):1404. doi: 10.1186/s12903-025-06796-4.
PubMed 40926256
Study at a glance
From the registry record, last updated by the sponsor on 8 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Accuracy of a Deep Learning Model (Artificial Intelligence) for Detecting Extra Root Canals in Mandibular Premolars on CBCT Images: Diagnostic Accuracy Study.
- ClinicalTrials.gov ID
- NCT07689708
- Acronym / sponsor ID
- 7.1.1
- Conditions
- Extra Canals
- Keywords
- AI, Mandibular premolars, extra canal
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 272 participants (estimated)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- 15 Jul 2026 (estimated)
- Primary completion
- 15 Aug 2026 (estimated)
- Study completion
- 10 Jul 2027 (estimated)
- First posted
- 8 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Successful endodontic treatment depends on the complete identification and management of the entire root canal system. Missed root canals are a major cause of endodontic failure, particularly in mandibular premolars, which exhibit considerable anatomical variability and may contain additional root canals that are difficult to detect using conventional diagnostic methods.
Cone Beam Computed Tomography (CBCT) provides three-dimensional visualization of root canal anatomy and has significantly improved the detection of anatomical variations. However, interpretation of CBCT images remains dependent on the experience and expertise of the clinician, leading to potential observer variability and missed diagnoses.
Recent advances in artificial intelligence (AI), particularly deep learning models based on convolutional neural networks, have shown promising results in dental image analysis and diagnostic support. AI-assisted diagnostic systems may improve the accuracy, consistency, and efficiency of CBCT interpretation by automatically identifying complex anatomical structures.
The aim of this retrospective diagnostic accuracy study is to evaluate the performance of a newly developed deep learning model for the detection of extra root canals in mandibular premolars using CBCT images. The diagnostic accuracy of the AI model will be assessed by comparing its findings with the assessments of experienced oral and maxillofacial radiologists, which will serve as the reference standard.
A total of 272 CBCT scans of mandibular premolars from Egyptian patients will be included according to predefined eligibility criteria. Diagnostic performance will be evaluated using measures including sensitivity, specificity, positive predictive value, and negative predictive value.
The findings of this study may provide evidence regarding the clinical applicability of AI-assisted diagnostic tools in endodontics and contribute to improved detection of complex root canal anatomy, reduced incidence of missed canals, and enhanced treatment outcomes.
Detailed description
The goal of this observational study is to evaluate whether a deep learning artificial intelligence (AI) model can accurately detect extra root canals in mandibular premolars using Cone Beam Computed Tomography (CBCT) images in Egyptian patients. The main questions it aims to answer are:
- Can the AI model accurately detect extra root canals in mandibular premolars on CBCT scans?
- Is the diagnostic accuracy of the AI model comparable to that of experienced oral and maxillofacial radiologists? Researchers will compare the results generated by the AI model with the assessments of experienced radiologists, which will serve as the reference standard.
Participants will:
- Provide previously acquired CBCT scans that meet the study eligibility criteria.
- Have their CBCT images analyzed by the AI model.
- Have their CBCT images independently evaluated by experienced radiologists for comparison with the AI findings.
The study findings may help determine the potential role of AI-assisted diagnostic tools in improving the detection of complex root canal anatomy and supporting endodontic diagnosis
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- It's an AI model to detect extra canals in mandibular premolars
| Arm | Type | Receives |
|---|---|---|
| Mandibular premolars with single canals | Experimental | Diagnostic Test: AI Model to detect any extra canals in mandibular premolars |
| Mandibular premolars with more than one canal | Experimental | Diagnostic Test: AI Model to detect any extra canals in mandibular premolars |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- CBCT scans of mandibular molars of Egyptian patients aging from 18 to 65 years old
- Small Field of view (FOV) including maximum a quadrant
- Voxel size not larger than 2mm
- Mandibular premolars showing complete root formation
- Carious or non-carious teeth
Key exclusion criteria (2 listed)
- Mandibular first and second premolars with developmental anomalies, external or internal root resorption, root canal calcification, previous root canal treatment, post restorations, and/or root caries
- CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI Model to detect any extra canals in mandibular premolarsIt is a study to detect the diagnostic accuracy of AI model to detect extra canals in mandibular premolars | Diagnostic test | Mandibular premolars with more than one canal, Mandibular premolars with single canals |
Primary outcome
Diagnostic Accuracy of the Deep Learning Model for Detection of Extra Root Canals in Mandibular Premolars
Time frame: During the procedure
Diagnostic accuracy of the AI model will be determined by comparison with expert radiologist assessment.
1 secondary outcome
Sensitivity of the AI Model Specificity of the AI Model Positive Predictive Value (PPV) Negative Predictive Value (NPV)
Time frame: During the procedure
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jul 2026.
- Results publication: PubMed 40926256Mansour S, Anter E, Mohamed AK, Dahaba MM, Mousa A. Two step approach for detecting and segmenting the second mesiobuccal canal of maxillary first molars on cone beam computed tomography (CBCT) images via artificial intelligence. BMC Oral Health. 2025 Sep 8;25(1):1404. doi: 10.1186/s12903-025-06796-4.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A study testing AI Model to detect any extra canals in mandibular premolars in people with Extra Canals, run by Cairo University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 8 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ayah Tarek, PHD candidate (20201221902479, ayahtarek94@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of the Deep Learning Model for Detection of Extra Root Canals in Mandibular Premolars (time frame: During the procedure). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07689708, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Jul 2026.